The spread of microbial infections is governed by the self-organization of bacteria on surfaces. Bacterial interactions in clinically relevant settings remain challenging to quantify, especially in systems with multiple species or varied material properties. Quantitative image analysis methods based on machine learning show promise to overcome this challenge and support the development of novel antimicrobial treatments, but are limited by a lack of high-quality training data. Here, novel experimental and image analysis techniques for high-fidelity single-cell segmentation of bacterial colonies are developed. Machine learning-based segmentation models are trained solely using synthetic microscopy images that are processed to look realistic using a state-of-the-art image-to-image translation method (cycleGAN), requiring no biophysical modeling. Accurate single-cell segmentation is achieved for densely packed single-species colonies and multi-species colonies of common pathogenic bacteria, even under suboptimal imaging conditions and for both brightfield and confocal laser scanning microscopy. The resulting data provide quantitative insights into the self-organization of bacteria on soft surfaces. Thanks to their high adaptability and relatively simple implementation, these methods promise to greatly facilitate quantitative descriptions of bacterial infections in varied environments, and may be used for the development of rapid diagnostic tools in clinical settings.

Smart health-care is the innovation that leads to enhanced diagnostic tools, improved patient treatment, and gadgets that ease the quality of life for majority of people. Textual clinical documents about an individual contain sensitive and semantically corelated terms. Most privacy-preserving approaches are not designed to prevent confidentiality threats. Although, recent approaches improved the utility of published output with generalized terms retrieved from several medical and general-purpose knowledge bases like SNOMED-CT and MASH. However, these models work on predefined sensitive terms using Wikipedia articles instead of authentic benchmarks. These Information Content-based methods are not capable to achieve the best balance between privacy and utility. The existing approaches guarantee syntactic privacy by sanitization but lack semantic privacy for textual clinical data. Therefore, it is imperative to design a confidentiality-aware framework to overcome these problems. Our proposed Confidentiality aware Textual Clinical Data Framework use preprocessed combinations of the terms instead of all combinations and perform automatic detection and sanitization of the sensitive and semantically correlated terms. The probabilistic sampling-based method guarantees the semantic privacy. We use high-level Petri nets to perform formal modeling of our proposed approach. Furthermore, we have also performed a detailed complexity analysis of the proposed framework.

Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles, into cells. Recently, research on CPP has gained substantial interest from researchers, and the biological mechanisms of CPPS have been assessed in the context of safe drug delivery agents and therapeutic applications. Correct identification and synthesis of CPPs using traditional biochemical methods is an extremely slow, expensive and laborious task particularly due to the large volume of unannotated peptide sequences accumulating in the World Bank repository. Hence, a powerful bioinformatics predictor that rapidly identifies CPPs with a high recognition rate is urgently needed. To date, numerous computational methods have been developed for CPP prediction. However, the available machine-learning (ML) tools are unable to distinguish both the CPPs and their uptake efficiencies. This study aimed to develop a two-layer deep learning framework named DeepCPPred to identify both CPPs in the first phase and peptide uptake efficiency in the second phase. The DeepCPPred predictor first uses four types of descriptors that cover evolutionary, energy estimation, reduced sequence and amino-acid contact information. Then, the extracted features are optimized through the elastic net algorithm and fed into a cascade deep forest algorithm to build the final CPP model. The proposed method achieved 99.45 percent overall accuracy with the CPP924 benchmark dataset in the first layer and 95.43 percent accuracy in the second layer with the CPPSite3 dataset using a 5-fold cross-validation test. Thus, our proposed bioinformatics tool surpassed all the existing state-of-the-art sequence-based CPP approaches.

Digital twins (DT) have been proposed to support and enhance manufacturing processes of the industries. The outcome of adopting DT is so encouraging that it is hoped that more than 50% of the large industries will benefit from DT by the end of 2021. Unfortunately, DT lacks a single publicly accepted narrative. In order to help researchers for building a common narrative about DT, we present an elaborated structure of DT, namely, spiral DT-framework. Furthermore, for a secure and reliable management of the DT data, we propose using the blockchain technology rather than cloud or fog. As the classical blockchain suffers from transaction confirmation delays and is vulnerable to the quantum attacks, therefore, we propose a new variant of blockchain, namely twinchain, which is quantum-resilient and offers immediate transaction confirmation. This article also presents a framework for deployment of twinchain for manufacturing of a robot surgical machine.

Due to the Internet of Things evolution, the clinical data is exponentially growing and using smart technologies. The generated big biomedical data is confidential, as it contains a patient’s personal information and findings. Usually, big biomedical data is stored over the cloud, making it convenient to be accessed and shared. In this view, the data shared for research purposes helps to reveal useful and unexposed aspects. Unfortunately, sharing of such sensitive data also leads to certain privacy threats. Generally, the clinical data is available in textual format (e.g., perception reports). Under the domain of natural language processing, many research studies have been published to mitigate the privacy breaches in textual clinical data. However, there are still limitations and shortcomings in the current studies that are inevitable to be addressed. In this article, a novel framework for textual medical data privacy has been proposed as Deep-Confidentiality . The proposed framework improves Medical Entity Recognition (MER) using deep neural networks and sanitization compared to the current state-of-the-art techniques. Moreover, the new and generic utility metric is also proposed, which overcomes the shortcomings of the existing utility metric. It provides the true representation of sanitized documents as compared to the original documents. To check our proposed framework’s effectiveness, it is evaluated on the i2b2-2010 NLP challenge dataset, which is considered one of the complex medical data for MER. The proposed framework improves the MER with 7.8% recall, 7% precision, and 3.8% F1-score compared to the existing deep learning models. It also improved the data utility of sanitized documents up to 13.79%, where the value of the k is 3.

Abstract The Internet of Things (IoT) is characterized by the seamless integration of heterogeneous devices into information networks to enable collaborative environments, specifically those concerning the collection of data and exchange of information and services. Security and trustworthiness are among the critical requirements for the effective deployment of IoT systems. However, trust management in IoT is extremely challenging due to its open environment, where the quality of information is often unknown because entities may misbehave. A hybrid context‐aware trust and reputation management protocol is presented for fog‐based IoT that addresses adaptivity, survivability, and scalability requirements. Through simulation, the effectiveness of the proposed protocol is demonstrated.

The Internet of Things (IoT) is gaining ground as a pervasive presence around us by enabling miniaturized “things” with computation and communication capabilities to collect, process, analyze, and interpret information. Consequently, trustworthy data act as fuel for applications that rely on the data generated by these things, for critical decision-making processes, data debugging, risk assessment, forensic analysis, and performance tuning. Currently, secure and reliable data communication in IoT is based on public-key cryptosystems such as the elliptic curve cryptosystem (ECC). Nevertheless, the reliance on the security of de-facto cryptographic primitives is at risk of being broken by the impending quantum computers. Therefore, the transition from classical primitives to quantum-safe primitives is indispensable to ensure the overall security of data en route. In this article, we investigate applications of one of the postquantum signatures called hash-based signature (HBS) schemes for the security of IoT devices in the quantum era. We give a succinct overview of the evolution of HBS schemes with an emphasis on their construction parameters and associated strengths and weaknesses. Then, we outline the striking features of HBS schemes and their significance for IoT security in the quantum era. We also investigate the optimal selection of HBS in the IoT networks with respect to their performance-constrained requirements, resource-constrained nature, and design optimization objectives. In addition to ongoing standardization efforts, we also highlight current and future research and deployment challenges along with possible solutions. Finally, we outline the essential measures and recommendations that must be adopted by the IoT ecosystem while preparing for the quantum world.

The industrial Internet of Things (IIoT) has the potential to transform several industries. However, reliable data collection, processing, and analytics cannot be performed if the underlying cloud storage is not trustworthy. The current IIoT architectures tend to adopt cloud computing as a backbone for their implementation and further deployment. Therefore, the trustworthiness of the cloud storage is of paramount importance for the design of efficient and reliable IIoT. This becomes very challenging if the data storage is outsourced to a third party, which may raise many problems such as noncompliance to service-level agreement, data theft, and privacy issues. Mobile agents have a number of characteristics including mobility, lightweight, autonomous, reactive, and intelligent making them ideal for deployment in many distributed applications. Therefore, in this article, we propose a multiagent-based approach to address the geolocation assurance problem of the outsourced data in the context of IIoT. As a result, we achieve efficient geolocation assurance with manageable costs without the need of a trusted third party.

With advances in fog and edge computing, various problems such as data processing for large Internet of Things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid (SG) IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for SGs incur high computation and communication costs, and in recent years, there have been efforts to leverage fog computing with SGs to overcome these limitations. In this article, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of the Paillier cryptosystem is used to encrypt the consumption data of the smart meter (SM) users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the SMs malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption, and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the privacy-preserving fog-enabled data aggregation scheme.

Privacy preserving data publishing (PPDP) refers to the releasing of anonymized data for the purpose of research and analysis. A considerable amount of research work exists for the publication of data, having a single sensitive attribute. The practical scenarios in PPDP with multiple sensitive attributes (MSAs) have not yet attracted much attention of researchers. Although a recently proposed technique (p, k)-Angelization provided a novel solution, in this regard, where one-to-one correspondence between the buckets in the generalized table (GT) and the sensitive table (ST) has been used. However, we have investigated a possibility of privacy leakage through MSA correlation among linkable sensitive buckets and named it as “fingerprint correlation fcorr attack.” Mitigating that in this paper, we propose an improved solution “ c,k -anonymization” algorithm. The proposed solution thwarts the fcorr attack using some privacy measures and improves the one-to-one correspondence to one-to-many correspondence between the buckets in GT and ST which further reduces the privacy risk with increased utility in GT. We have formally modelled and analysed the attack and the proposed solution. Experiments on the real-world datasets prove the outperformance of the proposed solution as compared to its counterpart.

Existing URL filtering mechanisms lacks support for real-time fault tolerance and scalability. In this paper these issues are addressed by developing a scalable model which is real time and fault tolerant to classify streams of URL traffic. The key feature of our model is that it saves computation time, resources usage and bandwidth. This model is implemented in Apache Spark which runs APIs for machine learning and streaming. The dataset consists of 2.4 million URLs which were taken from both clean and malicious classes. In training set, clean URLs are labeled as 1 and malicious are labeled as 0. For this proposed model, distributed in-memory computation is provided by Apache Spark's resilient distributed datasets (RDD) in fault tolerant manner. By increasing number of nodes in the cluster we achieved linear scalability. Our model attained an accuracy of 96% on logistic regression classifier and scaled up well with the Apache Spark's cluster. In 55 second using logistic regression classifier from Spark ML1ib, 2 million URLs can be filtered. The model achieved fl-score values of 0.92, 0.95 and 0.93 along with precision and the results are evaluated using cross-validation schemes.

Distributed computing environments merged into cloud computing architecture which is being used extensively for resource sharing. It has provided efficient and flexible ways for the provision of services meeting resource sharing needs and challenges of the time. Cloud computing has become a cost effective solution as compared to traditional systems. In cloud computing, Software, Infrastructure, and Platforms are being provided as services however this introduced new dimensions of research in security and privacy breaches. Trust establishment and its maintenance play a vital role in Cloud Computing. Availability, Security and Privacy are key parameters of trust and sharing of privacy preserved statistics of these parameters with stakeholders enhances but Security and Privacy lapses shatter users trust. This paper proposes a model of Trust Management System which provides a comprehensive component based architecture to facilitate cloud service providers in maintaining Users Trust and ultimately help them in flourishing their business.

Multiple traffic flows in a dense environment of a mono-sink wireless sensor network (WSN) experience congestion that leads to excessive energy consumption and severe packet loss. To address this problem, a Congestion Detection and Alleviation (CDA) mechanism has been proposed. CDA exploits the features and the characteristics of the sensor nodes and the wireless links between them to detect and alleviate node- and link-level congestion. Node-level congestion is detected by examining the buffer utilisation and the interval between the consecutive data packets. However, link-level congestion is detected through a novel procedure by determining link utilisation using back-off stage of Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). CDA alleviates congestion reactively by either rerouting the data traffic to a new less congested, more energy-efficient route or bypassing the affected node/link through ripple-based search. The simulation analysis performed in ns-2.35 evaluates CDA with Congestion Avoidance through Fairness (CAF) and with No Congestion Control (NOCC) protocols. The analysis shows that CDA improves packet delivery ratio by 33% as compared to CAF and 54% as compared to NOCC. CDA also shows an improvement in throughput by 16% as compared to CAF and 36% as compared to NOCC. Additionally, it reduces End-To-End delay by 17% as compared to CAF and 38% as compared to NOCC.

Whenever software is installed on a computer system, one has to agree to the end-user license agreement. The software license agreement grants licensee certain rights in software usage, but usually the ownership rights of the software stays with licensor. The licensor may also hold the right to restrict the usage of the software and can revoke the agreement if the licensee violates the license terms. Without agreeing with the license terms and conditions, the end-user is not authorized to use the software and could face penalties as described in the law. The main problem is that the percentage of the users who actually read the end-user license agreement which are almost incomprehensible for the average software user is very low. Mostly the end-user does not pay much attention to reading the license agreement because they believe that nearly all the end-user license agreements are practically the same. This misunderstanding makes them not fully read the agreement and just scroll the agreement and accept the terms and conditions. What they do not realize is that by breaking the license agreement they could be confronted with some penalties as described in the law. To overcome the problem of human inefficiency of understanding the license agreement, we have introduced a machine readable representation of the license agreement based on Semantic Web Technologies.

Mobile agent technology is an attempt towards bringing the computation process nearer to the physical location of data in a distributed system. Although it has been extensively studied and applied in a variety of applications but still its usage is limited mainly because of the security issues associated with its usage. Mobile agent watermark encapsulation (MAWE) is a method that first embeds a watermark in the execution results of a mobile agent (MA) and then the watermark results are encapsulated using digital signature. Any malicious changes in the results can be easily tracked by the owner of the mobile agent. In this paper we discuss the attacks that can be launched against Mobile agent watermark encapsulation (MAWE) and how the security properties for mobile agent data integrity are fulfilled. We try to model the behavior of a malicious host by launching a series of attacks against mobile agent and then we see to what extent the security properties for MA can be achieved. The experiments performed in a typical ticket booking scenario suggest that MAWE can be used to protect the results of MA.